There is known a diagnostic device as a tool for diagnosing a failure of a vehicle such as automobile. In recent years, there is considered that a so-called expert system for registering information on failures occurring in the past in database and estimating a cause of a newly occurring failure is used as the diagnostic device. The expert system has a rule base estimation system in which human experiences and knowledge are registered in database for search and a model base estimation system in which behaviors of the system during normal time or during failure are learned for searching a failure cause by simulation.
In the former system, a mechanism is simple and a relatively high reliability can be expected but the knowledge is difficult to rule and a rule needs to be added or modified each time the target system is changed.
On the other hand, in the latter system, even if an operating staff does not have sufficient experience or knowledge, a failure site can be estimated and a certain degree of versatility can be expected in terms of the change in the target system. Particularly, a method for comparing data during normal time with data during failure under the same driving environmental condition is one of the most effective means for finding a cause of a failure.
However, since various environmental conditions or travel patterns are present for the driving of a vehicle and a range of normal values, which a large number of respective driving parameters can take depending on a respective situation, can be independently changed, the data during normal time needs to be collected under various driving situations in order to obtain normal value data for comparing items of data under similar conditions and to make an accurate decision.
There is described in Japanese Patent Application Laid-Open No. 62-261938 (Patent Literature 1) a diagnostic device comprising knowledge data storing means for storing accurate information on a relationship between a failure symptom and a corresponding cause and rare case storing means for storing inaccurate information.
Japanese Patent Application Laid-Open No. 6-95881 Publication (Patent Literature 2) describes therein analyzing design data or past failure data and inputting it as EMEA in a positive estimation system in relational database, creating a modified EMEA and then an event sequence diagram, creating a failure retrieval three for reference to a rule base, and digitizing an expert know-how to create a rule base.
Since an extremely large number of driving parameters are present and normal value ranges of the respective driving parameters are present corresponding to driving environments (which may be referred to as driving conditions) in a complicated electronic control system used in a vehicle, it is so difficult to extract reference ECU data (in which the respective driving parameters are in normal value ranges) in a driving environment near ECU data (inspection data) to be diagnosed from among a large number of items of normal value data. Since many devices cooperate for control, if one failure occurs, multiple driving parameters are influenced and are likely to be deviated from the normal values. Thus, the associations between the driving parameters out of the normal values and the failure causes need to be considered.
As one example for the associations, there is described in Japanese Patent Application Laid-Open No. 2003-15877 (Patent Literature 3) that a qualitative processing is performed on process data obtained from events to be monitored to calculate similarities between the obtained qualitative data and the qualitative data for all the cases and case data is extracted in descending order of similarity for the data having a certain similarity from among all the cases.
The more the number of parameters on driving is, the more the number of driving parameters out of the normal value data ranges is when a failure occurs, and it is important to accurately judge the driving parameters in direct relation with the failure cause and to make a failure diagnosis. In other words, it is important to accurately select a parameter to be paid attention from among the driving parameters out of the normal value ranges.